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Found 13 papers, 2 papers with code
Metric Learning to Accelerate Convergence of Operator Splitting Methods for Differentiable Parametric Programming
Recent work has shown a variety of ways in which machine learning can be used to accelerate the solution of constrained optimization problems.
Learning Constrained Optimization with Deep Augmented Lagrangian Methods
Learning to Optimize (LtO) is a problem setting in which a machine learning (ML) model is trained to emulate a constrained optimization solver.
End-to-End Learning for Fair Multiobjective Optimization Under Uncertainty
Many decision processes in artificial intelligence and operations research are modeled by parametric optimization problems whose defining parameters are unknown and must be inferred from observable data.
Learning Fair Ranking Policies via Differentiable Optimization of Ordered Weighted Averages
Learning to Rank (LTR) is one of the most widely used machine learning applications.
Analyzing and Enhancing the Backward-Pass Convergence of Unrolled Optimization
The integration of constrained optimization models as components in deep networks has led to promising advances on many specialized learning tasks.
Predict-Then-Optimize by Proxy: Learning Joint Models of Prediction and Optimization
This paper proposes an alternative method, in which optimal solutions are learned directly from the observable features by predictive models.
Decision-Focused Learning: Foundations, State of the Art, Benchmark and Future Opportunities
Decision-focused learning (DFL) is an emerging paradigm in machine learning which trains a model to optimize decisions, integrating prediction and optimization in an end-to-end system.
Backpropagation of Unrolled Solvers with Folded Optimization
A central challenge in this setting is backpropagation through the solution of an optimization problem, which typically lacks a closed form.
Differentiable Model Selection for Ensemble Learning
Model selection is a strategy aimed at creating accurate and robust models.
End-to-end Learning for Fair Ranking Systems
The end-to-end SPOFR framework includes a constrained optimization sub-model and produces ranking policies that are guaranteed to satisfy fairness constraints while allowing for fine control of the fairness-utility tradeoff.
Fast Approximations for Job Shop Scheduling: A Lagrangian Dual Deep Learning Method
The Jobs shop Scheduling Problem (JSP) is a canonical combinatorial optimization problem that is routinely solved for a variety of industrial purposes.
Learning Hard Optimization Problems: A Data Generation Perspective
Optimization problems are ubiquitous in our societies and are present in almost every segment of the economy.
End-to-End Constrained Optimization Learning: A Survey
This paper surveys the recent attempts at leveraging machine learning to solve constrained optimization problems.
